Defining HIV Susceptibility to New Antiretroviral Agents—Darunavir
- Department of Medicine, Division of Infectious Diseases, University California San Diego, San Diego, California
- Reprints or correspondence: Dr. Richard H. Haubrich, Antiviral Research Center, 150 W. Washington St., Ste. 100, San Diego, CA 92103 (rhaubrich{at}ucsd.edu)
As the number of agents that are used to treat HIV increases, the decisions regarding which antiretroviral agents to select in each clinical setting become more complex. The choice of the components of the regimen for patients who have an extensive treatment history relies on a careful assessment of (1) that past treatment history, (2) the response to prior agents, and (3) the results of past and current resistance tests [1]. In the absence of transmitted resistance to a drug, the virologic response to a drug from a class that a patient has used is almost always less than the response to a drug from a class that a patient has never used [2, 3]. The purpose of resistance testing is to help us to define which agent(s) of a class will have the best residual activity [1, 4,5]; however, the interpretation of the resistance-test results can be difficult. For example, in the protease inhibitor (PI) class, the patterns of the PI resistance—associated mutations are complex and vary from drug to drug. PI resistance—associated mutations can be identified by in vitro selection experiments, by cataloging the appearance of new mutations during therapy, or by relating virologic response to a drug in patients with existing mutations. Ideally, the genotypic algorithm that defines the activity of each drug is based on large data sets, in which the patterns of the mutations from a baseline genotype can be interpreted in relation to HIV-RNA responses to a new regimen containing the drug of interest [6]. The limitations of the earlier studies are that the sample sizes are small, compared with the large number of possible genotypic mutational patterns, and that most of their analyses define the relative, as opposed to the actual, potency of a drug in the regimen.
When new antiretroviral drugs are submitted to regulatory authorities for review, information about the effects that both genotypic and phenotypic resistance to drugs has on the predicted response to the drug is provided [7]. Indeed, the agents approved for use in the past several years have had, at the time of market release, significantly more drug-resistance information available than have earlier agents. However, the mutation patterns that are present in the study populations may not fully define all of the mutations that could lead to a reduced response to an agent. Additional data, from clinical trials or observational databases, can augment the interpretation algorithms for new agents. In this issue of the Journal, Mitsuya et al.'s brief report [8], which suggests that the mutational scores derived from the clinical trials to date may be incomplete for the interpretation of clinical resistance, represents a step in the right direction for the recently approved drug darunavir (DRV).
The preliminary analyses from the POWER studies found 11 mutations (V11I, V32I, L33F, I47V, I50V, I54L, I54M, G73S, L76V, I84V, and L89V) that were associated with a reduced response to a DRV-containing regimen [9]. These analyses used a plasma HIV-RNA level of <50 copies/mL at 24 weeks as the outcome variable. DRV resistance—associated mutations were defined as those which were present in subjects who were less likely to achieve viral suppression, compared with the overall response of the entire population; increasing the number of these mutations was associated with a reduction in phenotypic susceptibility and virologic response. However, the accumulation of PI mutations defined by the International AIDS Society—USA [10] was not as closely linked to viral responses.
These analyses have a number of limitations: (1) they do not account for the effects of other drugs in the regimen (DRV was initiated with an optimized background of nucleoside reverse-transcriptase inhibitors [NRTIs], with or without enfuvirtide); (2) they do not weight mutations that might have a bigger impact on virologic response; and (3) the selected end point (viral suppression at 24 weeks) may have been more dependent on the regimen containing adequate numbers of active drugs rather than on the intrinsic potency of DRV against the PI-resistant isolates. Further sensitivity analyses (using a genotype- or phenotype-sensitivity score [6, 11–13]) should be performed with the data set, to account for other agents in the regimen and to explore other end points. For these analyses, using intent-to-treat data, which might ascribe failure to administrative reasons in addition to HIV-RNA levels of >50 copies/mL, may be less informative than as-treated data. For example, in the package insert for DRV, a different analysis used as-treated data to relate the number of amino acid changes at positions 30, 32, 36, 46–48, 50, 53, 54, 73, 82, 84, 88, or 90 (any change was counted) to the ⩾1-log10-copies/mL reduction of HIV RNA at week 24 [14]; for patients with 0–4 PI mutations, the response was 81%, for patients with 5–6 mutations, it was 76%, and for patients with 7 or more mutations, it was 21%. Thus, with the same data set but with different analytic methods, 2 different algorithms have been generated; potentially, for an individual patient, the 2 algorithms could give 2 different estimations of the likelihood of response to a regimen that contains DRV.
How do the results reported by Mitsuya et al. contribute to our understanding of susceptibility and response to DRV? The investigators conducted a retrospective cohort analysis using 2 databases. The prevalence of DRV resistance—associated mutations, based on the list from De Meyer et al. [9], was determined for both data sets. Not surprisingly, the prevalence of these resistance-associated mutations in PI-naive patients was low in both data sets, but the prevalence among PI-experienced patients was high, nearly 30% in 1 data set. As expected, the increase in exposure to PI and the use of amprenavir increased the number of DRV resistance—associated mutations. Mitsuya et al. comment that the majority of patients in these cohorts would have fewer than 3 DRV resistance—associated mutations and would likely have a good response to DRV-based therapy, which is certainly good news for the population of triple-class—experienced patients.
Importantly, the authors also found 17 additional mutations that were statistically associated with the presence of at least 1 of the DRV mutations in De Meyer et al.'s list, which implies that there may be other DRV resistance—associated mutations that, when present, may impact the response to DRV-based regimens. One explanation for the identification of different mutations is that the populations evaluated by Mitsuya et al. were different from the subjects evaluated in the POWER studies. Most notably, the proportion of subjects with exposure to 1 or 2 PIs was 61%–81% in the cohort studied by Mitsuya et al., compared with 95%–100% in the POWER studies [15, 16]. In addition, the populations studied by Mitsuya et al. used more nelfinavir (48%) and less amprenavir (8%). Likely, the degree of DRV resistance in the Mitsuya et al. cohorts may be less than that in other clinic populations, especially those in whom there is greater breadth and diversity of PI use.
One major limitation of the Mitsuya et al. analysis is the lack of clinical-response data that would help us to evaluate whether these additional mutations are associated with a reduced virologic response to DRV-containing regimens. Further work, using data sets with genotypic- and phenotypic-resistance assays and virologic-response data, is needed to validate the De Meyer et al. set of resistance-associated mutations and to see how the additional Mitsuya et al. mutations improve treatment-response predictions. Cohort databases are powerful research tools, but they do not allow the same rigor that prospective clinical trials do; they have neither the same level of data-quality assurance nor the breadth of clinical covariates, such as past treatment history, toxicity, and adherence. The DUET studies (large randomized studies of TMC125, a new NNRTI active against NNRTI-resistant virus) used DRV in all the regimens; the further refinement of genotypic algorithms could be an added benefit of secondary analyses of these data sets.
Because current algorithms have been designed for single drugs and have not been compared across available drug treatment options, De Meyer et al.'s and Mitsuya et al.'s mutation sets are, at present, incomplete, and this complicates their use in the clinic. Ideally, genotypic-resistance algorithms would report the relative susceptibility of DRV compared with the predicted susceptibility of other PIs. For example, it is not clear whether the presence of 3 mutations from the DRV set, rather than from another drug, such as tipranavir, might predict a better response if DRV were used in a regimen. In this case, phenotype assays may add additional information about the relative activity of the available PIs [1].
Even after the clinician identifies the optimal PI, many questions remain concerning the selection of other drugs for the regimen. Even if the resistance test suggests that DRV is the optimal PI, it is still unclear how much activity can be ascribed to that drug and how many additional agents might be needed to achieve viral suppression. Many recent studies have confirmed that the addition of a new class of agents—including a fusion inhibitor (enfuvirtide), a CCR5 antagonist (maraviroc), or an integrase inhibitor (raltegravir)—greatly increases the likelihood of achieving HIV-RNA levels of <50 copies/mL [17–21]. Current guidelines now recommend that viral suppression be the goal of therapy, even in extensively treatment-experienced patients, and that at least 2, optimally 3, active agents be used in a new regimen [2]. But when can we consider DRV or other agents from recycled classes to be fully active agents? How many agents, and from which classes, need to be combined in the regimen? These and other questions remain.
Although recent studies of treatment-experienced patients have demonstrated the benefit of new agents, additional research is needed to evaluate strategies of regimen selection in heavily treated populations. To that end, the AIDS Clinical Trials Group is developing A5241, a randomized study that will attempt to answer 2 questions: (1) Are NRTIs necessary when more than 2 active agents are present in a new regimen? (2) Can a phenotypic susceptibility score be used to "add up" the relative activity of recycled agents and to define the threshold level at which sufficient activity is present in a regimen, so as to not add all possible new or recycled drug classes? This and similar studies will help to define the way forward into a new era of antiretroviral therapy in which high rates of viral suppression can be achieved in HIV-infected patients at all stages of the treatment continuum.
Acknowledgments
I thank Drs. Davey Smith and Miguel Goicoechea for a critical review of the manuscript.
Footnotes
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Potential conflicts of interest: R.H. has received grant support (through the University of California) from Tibotec.
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Financial support: National Institutes of Health (grant K24-AI064086); UCSD AIDS Clinical Trials Unit (grant AI 27670); the UCSD Center for AIDS Research (grant SP30 AI36214); California Universitywide AIDS Research Program: California Collaborative Treatment Group (grant CH05-SD-607-005).
- Received May 13, 2007.
- Accepted May 14, 2007.
- © 2007 by the Infectious Diseases Society of America
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